bank credit Risk estimating Using RBF Artificial Neural Network model: The Popular Credit Bank of Algeria as case study
Abstract
This study aims to estimate the credit risk in Algerian banks and make the right lending decision using the RBF radial basis function artificial neural network model. To achieve this, we formed a database of financial and qualitative variables for 100 of borrowing institutions From the CPA bank, this sample divided into 50 good Borrower institutions and 50 other bad borrower institutions. To create the RBF model, we used SPSS(V25) program.
The results of the study showed that the artificial neural networks model had shown an accuracy in classification at 100%, which would help Algerian banks to predict credit risks and make wise and speed lending's decision more than the classic models, but these modern approaches require robust technologies devices and quantitative and statistical methods.
References
2. hadji Amel.(2015-2016).Le risque de crédit de l’évaluation traditionnelle au Scoring. Université Djillali Liabes..
3. Han Lu, Han Liyan Zhao Hongwei. (2010). Combined Model of Empirical Study for Credit Risk Management. 2010 2nd IEEE International Conference on Information and Financial Engineering.
4. Hussain Ali Bekhet Shorouq Fathi Kamel Eletter. (2014). Credit risk assessment model for Jordanian commercial banks: Neural scoring approach. Review of Development Finance، 20-28.
5. Ilyes Abid,Rim Ayadi and others. (2022). A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction. Annals of Operations Research ، 605–623.
6. KALYAN DAS and others. (2004). MEAN SQUARED ERROR OF EMPIRICAL PREDICTOR. The Annals of Statistics، 818-840
7. Nnamdi I. Nwulu, Shola Oroja & Mustafa Ilkan . (2011). Credit Scoring Using Soft Computing Schemes: A Comparison between Support Vector Machines and Artificial Neural Networks. DEIS 2011: Digital Enterprise and Information Systems، 275–286.
8. Pierre Mathieu ; Patrick D'Herouville. (1998). Les dérivés de crédit : une nouvelle gestion du risque de crédit. Paris: Economica paris.
9. Shuai Li, Yuanmei Zhu, Chao Xu, Zongfang Zhou. (2013). Study of Personal Credit Evaluation Method Based on PSO-RBF Neural Network Model. American Journal of Industrial and Business Management، 429-434.
10. Sulin Pang. (2005). Credit scoring model based on radial basis function network. IEEE International Workshop on VLSI Design and Video Technology, 2005.
11. Vytautas Boguslauskas, Ricardas Mileris. (2009). Estimation of Credit Risk by Artificial Neural Networks Models. Izinerine Ekonomika-Engineering Economics(4)-Kaunas University of Technology ، 7-14.
12. Wu Yunna， Si Zhaomin. (2008). Application of RBF Neural Network Based on Ant Colony Algorithm in Credit Risk Evaluation of Construction Enterprises. The 2008 International Conference on Risk Management & Engineering Management.
13. younes Boujelbène, Sihem Khemakhem. (2013). Prévision du risque de crédit : Une étude comparative entre l’Analyse Discriminante et l’Approche Neuronale. HAL open science.
14. Yusuf Ali Khalaf Al-Hroot. (2016). Bankruptcy Prediction Using Multilayer Perceptron Neural Networks In Jordan. European Scientific Journal February vol.12، 425-435
